Computer-based Self-training for CT Colonography with and Without CAD
Overview
Authors
Affiliations
Objectives: To determine whether (1) computer-based self-training for CT colonography (CTC) improves interpretation performance of novice readers; (2) computer-aided detection (CAD) use during training affects learning.
Methods: Institutional review board approval and patients' informed consent were obtained for all cases included in this study. Twenty readers (17 radiology residents, 3 radiologists) with no experience in CTC interpretation were recruited in three centres. After an introductory course, readers performed a baseline assessment test (37 cases) using CAD as second reader. Then they were randomized (1:1) to perform either a computer-based self-training (150 cases verified at colonoscopy) with CAD as second reader or the same training without CAD. The same assessment test was repeated after completion of the training programs. Main outcome was per lesion sensitivity (≥ 6 mm). A generalized estimating equation model was applied to evaluate readers' performance and the impact of CAD use during training.
Results: After training, there was a significant improvement in average per lesion sensitivity in the unassisted phase, from 74% (356/480) to 83% (396/480) (p < 0.001), and in the CAD-assisted phase, from 83% (399/480) to 87% (417/480) (p = 0.021), but not in average per patient sensitivity, from 93% (390/420) to 94% (395/420) (p = 0.41), and specificity, from 81% (260/320) to 86% (276/320) (p = 0.15). No significant effect of CAD use during training was observed on per patient sensitivity and specificity, nor on per lesion sensitivity.
Conclusions: A computer-based self-training program for CTC improves readers' per lesion sensitivity. CAD as second reader does not have a significant impact on learning if used during training.
Key Points: • Computer-based self-training for CT colonography improves per lesion sensitivity of novice readers. • Self-training program does not increase per patient specificity of novice readers. • CAD used during training does not have significant impact on learning.
Local Recurrences in Rectal Cancer: MRI vs. CT.
Grazzini G, Danti G, Chiti G, Giannessi C, Pradella S, Miele V Diagnostics (Basel). 2023; 13(12).
PMID: 37370997 PMC: 10296819. DOI: 10.3390/diagnostics13122104.
Hernandez-Rodriguez J, Rodriguez-Conde M, Santos-Sanchez J, Cabrero-Fraile F Heliyon. 2023; 9(4):e14780.
PMID: 37025816 PMC: 10070709. DOI: 10.1016/j.heliyon.2023.e14780.
Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe?.
Mascalchi M, Picozzi G, Puliti D, Gorini G, Mantellini P, Sali L Diagnostics (Basel). 2022; 12(10).
PMID: 36292015 PMC: 9601268. DOI: 10.3390/diagnostics12102326.
Giannini V, Mazzetti S, Cappello G, Doronzio V, Vassallo L, Russo F Diagnostics (Basel). 2021; 11(6).
PMID: 34071215 PMC: 8227686. DOI: 10.3390/diagnostics11060973.
Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning.
Yin C, Chen Z Healthcare (Basel). 2020; 8(3).
PMID: 32846941 PMC: 7551840. DOI: 10.3390/healthcare8030291.